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Based on experimental comparison, this paper discusses approximate solution methods of medium-scale traveling salesman problems (TSPs) that suit repetitive use in interactive simulation for globally optimizing a large-scale distribution logistic network. For constructing a globally optimized large-scale logistic network, the problem is decomposed into hundreds of sub-problems. And each sub-problem including above-mentioned TSPs should be repetitively solved. Thus, it is essential to find approximate solution methods of medium-scale TSPs that suit the heavily repetitive use in interactive simulation for globally optimizing a large-scale distribution logistic network. Accordingly, we made an experiment for comparison among approximate methods using random restart strategy that iterates the combination of random initialization and local search. As a result of this experimental comparison, we discovered one of above approximate methods could obtain solutions ensuring errors below 2-3% within 0.1 second.Thus, this method is considered promising to realize the system that enables to make above-mentioned interactive simulations repetitively for constructing a globally optimized large-scale logistic network.

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